Data analysis in short track speed skating has become a crucial tool for improving athlete performance, optimizing training strategies, and enhancing competitive outcomes. This study explores the application of data-driven techniques in short-track speed skating, focusing on performance metrics including lap times, split times, acceleration, and biomechanical efficiency. By leveraging sensors, motion capture systems, and wearable technology, coaches and analysts can collect real-time data on skaters' movements, allowing for detailed analysis of stroke mechanics, glide phases, and cornering techniques. The integration of this data into performance modeling helps identify strengths and weaknesses, fine-tune race strategies, and predict potential outcomes under specific conditions. Additionally, machine learning algorithms are increasingly being employed to predict injury risk and optimize training regimens. This paper discusses methods of data collection and analysis, and the practical applications of the insights gained, highlighting the potential to improve both individual and team performance in short-track speed skating. Ultimately, data analysis in this domain offers a significant competitive edge, driving the evolution of techniques and advancing sport to new levels of precision and excellence.
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